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Computational thinking in early childhood is underpinned by sequencing ability and self-regulation: a cross-sectional study

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Abstract

Computational thinking (CT) is a new literacy of 21st century that can be transferred to and applied in different real-world situations, although being derived from the discipline of computer science. Tangible robots or child-friendly digital apps are used to implement coding education with the goal of promoting young children’s CT. However, there are still controversies on the validity and applicability of CT in early childhood, mainly due to the vagueness of the learning mechanism underlying young children’s CT. This cross-sectional study examined the associations among sequencing ability, self-regulation and CT among Chinses preschoolers (N = 101, Mage = 5.25 years, SD = 0.73). Results showed that sequencing ability and self-regulation have positive and significant associations with CT, and the relationship between sequencing ability and CT was fully mediated by self-regulation, even after controlling for child gender, age, and family socioeconomic status (SES). This implies CT in early childhood as a combination of sequencing ability and self-regulation. Findings of this study have implications for early childhood CT education programs, suggesting the need to assist children in learning sequencing and how to self-regulate in coding (both plugged and unplugged) and STEM activities.

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Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.a

References

  • Ahmed, S. F., Tang, S., Waters, N. E., & Davis-Kean, P. (2019). Executive function and academic achievement: Longitudinal relations from early childhood to adolescence. Journal of Educational Psychology, 111(3), 446–458. https://doi.org/10.1037/edu0000296.

    Article  Google Scholar 

  • Ambrosio, A. P., Xavier, C., & Georges, F. (2014). Digital ink for cognitive assessment of computational thinking. In 2014 IEEE Frontiers in Education Conference (pp. 1520–1526). https://doi.org/10.1109/FIE.2014.7044237

  • Arfé, B., Vardanega, T., Montuori, C., & Lavanga, M. (2019). Coding in primary grades boosts children’s executive functions. Frontiers in Psychology, 10, 2713. https://doi.org/10.3389/fpsyg.2019.02713.

    Article  Google Scholar 

  • Arfé, B., Vardanega, T., & Ronconi, L. (2020). The effects of coding on children’s planning and inhibition skills. Computers & Education, 148, 103807. https://doi.org/10.1016/j.compedu.2020.103807.

    Article  Google Scholar 

  • Baumeister, R. F., & Vohs, K. D. (2003). Self-regulation and the executive function of the self. Handbook of Self and Identity, 1, 197–217.

    Google Scholar 

  • Berk, L. E. (2003). Child Development Allyn and Bacon.

  • Bers, M. U. (2018). Coding as a playground: Programming and computational thinking in the early childhood classroom. Routledge.

  • Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145–157. https://doi.org/10.1016/j.compedu.2013.10.020.

    Article  Google Scholar 

  • Bodrova, E., & Leong, D. J. (2006). Self-regulation as a key to school readiness: How early childhood teachers can promote this critical competency. In M. Zaslow, & I. Martinez-Beck (Eds.), Critical issues in early childhood professional development (pp. 203–224). Paul H Brookes Publishing.

  • Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada (Vol. 1, p. 25).

  • Brock, L. L., Rimm-Kaufman, S. E., Nathanson, L., & Grimm, K. J. (2009). The contributions of “hot” and “cool” executive function to children’s academic achievement, learning-related behaviors, and engagement in kindergarten. Early Childhood Research Quarterly, 24(3), 337–349. https://doi.org/10.1016/j.ecresq.2009.06.001.

    Article  Google Scholar 

  • Brown, A. L. (1975). Recognition, reconstruction, and recall of narrative sequences by preoperational children. Child Development, 46(1), 156–166. https://doi.org/1128844

  • Bull, R., & Scerif, G. (2001). Executive function as a predictor of children’s mathematics ability: Inhibition, switching, and working memory. Developmental Neuropsychology, 19, 273–293. https://doi.org/10.1207/S15326942DN1903_3.

    Article  Google Scholar 

  • Cameron Ponitz, C., McClelland, M. M., Matthews, J. S., & Morrison, F. J. (2009). A structured observation of behavioral self-regulation and its contribution to kindergarten outcomes. Developmental Psychology, 45(3), 605–619. https://doi.org/10.1037/a0015365.

    Article  Google Scholar 

  • Catalsakal, S. (2016). How trait mindfulness is related to job performance and job satisfaction: self-regulation as a potential mediator. Master’s thesis, Middle East Technical University.

  • Clements, D. H. (1987). Longitudinal study of the effects of Logo programming on cognitive abilities and achievement. Journal of Educational Computing Research, 3(1), 73–94. https://doi.org/10.2190%2FRCNV-2HYF-60CM-K7K7.

    Article  MathSciNet  Google Scholar 

  • Corno, L., & Mandinach, E. B. (1983). The role of cognitive engagement in classroom learning and motivation. Educational Psychologist, 18, 88–108. https://doi.org/10.1080/00461528309529266.

    Article  Google Scholar 

  • Di Lieto, M. C., Inguaggiato, E., Castro, E., Cecchi, F., Cioni, G., Dell’Omo, M., & Dario, P. (2017). Educational robotics intervention on executive functions in preschool children: A pilot study. Computers in Human Behavior, 71, 16–23. https://doi.org/10.1016/j.chb.2017.01.018.

    Article  Google Scholar 

  • Eiden, R. D., Edwards, E. P., & Leonard, K. E. (2007). A conceptual model for the development of externalizing behavior problems among kindergarten children of alcoholic families: Role of parenting and children’s self-regulation. Developmental Psychology, 43(5), 1187–1201.

    Article  Google Scholar 

  • Eisenberg, N., Valiente, C., & Eggum, N. D. (2010). Self-regulation and school readiness. Early Education and Development, 21(5), 681–698. https://doi.org/10.1080/10409289.2010.497451.

    Article  Google Scholar 

  • Elkin, M., Sullivan, A., & Bers, M. U. (2016). Programming with the KIBO robotics kit in preschool classrooms. Computers in the Schools, 33(3), 169–186. https://doi.org/10.1080/07380569.2016.1216251.

    Article  Google Scholar 

  • Espy, K., McDiarmid, M., Kwik, M., Stalets, M., Hamby, A., & Senn, T. (2004). The contribution of executive functions to emergent mathematics skills in preschool children. Developmental Neuroscience, 26, 465–486. https://doi.org/10.1207/s15326942dn26016.

    Article  Google Scholar 

  • Fedorenko, E., Ivanova, A., Dhamala, R., & Bers, M. U. (2019). The language of programming: A cognitive perspective. Trends in Cognitive Sciences, 23(7), 525–528. https://doi.org/10.1016/j.tics.2019.04.010.

    Article  Google Scholar 

  • Ganesalingam, K., Sanson, A., Anderson, V., & Yeates, K. (2007). Self-regulation as a mediator of the effects of childhood traumatic brain injury on social and behavioral functioning. Journal of the International Neuropsychological Society, 13(2), 298–311. https://doi.org/10.1017/S1355617707070324.

    Article  Google Scholar 

  • Gerosa, A., Koleszar, V., Tejera, G., Gómez-Sena, L., & Carboni, A. (2021). Cognitive abilities and computational thinking at age 5: Evidence for associations to sequencing and symbolic number comparison. Computers and Education Open, 2, 100043. https://doi.org/10.1016/j.caeo.2021.100043.

    Article  Google Scholar 

  • Gestsdottir, S., von Suchodoletz, A., Wanless, S. B., Hubert, B., Guimard, P., Birgisdottir, F., & McClelland, M. (2014). Early behavioral self-regulation, academic achievement, and gender: Longitudinal findings from France, Germany, and Iceland. Applied Developmental Science, 18(2), 90–109. https://doi.org/10.1080/10888691.2014.894870.

    Article  Google Scholar 

  • Gignac, G. E., & Szodorai, E. T. (2016). Effect size guidelines for individual differences researchers. Personality and Individual Differences, 102, 74–78. https://doi.org/10.1016/j.paid.2016.06.069.

    Article  Google Scholar 

  • Gözüm, A. İ. C., & Aktulun, Ö. U. (2021). Relationship between Pre-Schoolers’ self-regulation, language, and early academic skills: The mediating role of self-regulation and moderating role of gender. Current Psychology, 40(10), 4718–4740. https://doi.org/10.1007/s12144-021-01699-3.

    Article  Google Scholar 

  • Hamilton, R., Vohs, K. D., Sellier, A. L., & Meyvis, T. (2011). Being of two minds: Switching mindsets exhausts self-regulatory resources. Organizational Behavior and Human Decision Processes, 115(1), 13–24. https://doi.org/10.1016/j.obhdp.2010.11.005.

    Article  Google Scholar 

  • Hayes, A. F. (2012). PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling. http://www.afhayes.com/public/process2012.pdf

  • Highfield, K. (2000). Robotic toys as a catalyst for mathematical problem solving. Australian Primary Mathematics Classroom, 15(2), 22–27.

    Google Scholar 

  • Hsu, T. C., Chang, S. C., & Hung, Y. T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296–310. https://doi.org/10.1016/j.compedu.2018.07.004.

    Article  Google Scholar 

  • Kazakoff, E. (2014). Cats in Space, Pigs that Race: Does self-regulation play a role when kindergartners learn to code? Unpublished doctoral dissertation, Tufts University.

  • Kazakoff, E., & Bers, M. (2012). Programming in a robotics context in the kindergarten classroom: The impact on sequencing skills. Journal of Educational Multimedia and Hypermedia, 21(4), 371–391.

    Google Scholar 

  • Kazakoff, E. R., & Bers, M. U. (2014a). Put your robot in, put your robot out: Sequencing through programming robots in early childhood. Journal of Educational Computing Research, 50(4), 553–573. https://doi.org/10.2190%2FEC.50.4.f.

    Article  Google Scholar 

  • Kazakoff, E. R., & Bers, M. U. (2014b). Put your robot in, put your robot out: Sequencing through programming robots in early childhood. Journal of Educational Computing Research, 50(4), 553–573. https://doi.org/10.2190%2FEC.50.4.f.

    Article  Google Scholar 

  • Kazakoff, E. R., Sullivan, A., & Bers, M. U. (2013). The effect of a classroom-based intensive robotics and programming workshop on sequencing ability in early childhood. Early Childhood Education Journal, 41(4), 245–255. https://doi.org/10.1007/s10643-012-0554-5.

    Article  Google Scholar 

  • Liu, Y. C., Huang, T. H., & Sung, C. L. (2021). The determinants of impact of personal traits on computational thinking with programming instruction.Interactive Learning Environments,1–15.

  • Lu, J. J., & Fletcher, G. H. (2009). Thinking about computational thinking. Proceedings of the 40th ACM Technical Symposium on Computer Science Education, 260–264. https://doi.org/10.1145/1508865.1508959

  • Lucas-Nihei, J. N. (2020). Mediating the Relation Between Parent-Child Attachment Relationships and Peer Acceptance with Preschoolers’ Self-Regulation Doctoral dissertation, Illinois State University.

  • Manches, A., & Plowman, L. (2017). Computing education in children’s early years: A call for debate. British Journal of Educational Technology, 48(1), 191–201. https://doi.org/10.1111/bjet.12355.

    Article  Google Scholar 

  • Mazzocco, M. M. M., & Kover, S. T. (2007). A longitudinal assessment of executive function skills and their association with math performance. Child Neuropsychology, 13, 18–45. https://doi.org/10.1080/09297040600611346.

    Article  Google Scholar 

  • McClelland, M. M., & Cameron, C. E. (2012). Self-regulation in early childhood: Improving conceptual clarity and developing ecologically valid measures. Child Development Perspectives, 6(2), 136–142. https://doi.org/10.1111/j.1750-8606.2011.00191.x.

    Article  Google Scholar 

  • McClelland, M. M., Cameron, C. E., Connor, C. M., Farris, C. L., Jewkes, A. M., & Morrison, F. J. (2007). Links between behavioral regulation and preschoolers’ literacy, vocabulary, and math skills. Developmental Psychology, 43(4), 947. https://doi.org/10.1037/0012-1649.43.4.947.

    Article  Google Scholar 

  • McClelland, M. M., Cameron, C. E., Duncan, R., Bowles, R. P., Acock, A. C., Miao, A., & Pratt, M. E. (2014). Predictors of early growth in academic achievement: The head-toes-knees-shoulders task. Frontiers in Psychology, 5, 599. https://doi.org/10.3389/fpsyg.2014.00599.

    Article  Google Scholar 

  • Morosanova, V. I. (2013). Self-regulation and personality. Procedia-Social and Behavioral Sciences, 86, 452–457. https://doi.org/10.1016/j.sbspro.2013.08.596.

    Article  Google Scholar 

  • Myers, E. K. (2021). The role of executive function and self-regulation in the development of computational thinking. In M. Bers (Ed.), Teaching computational thinking and coding to young children (pp. 64–83). IGI Global.

  • Neuman, S. B., & Dickinson, D. K. (Eds.). (2002). Handbook of early literacy research. Guilford Press.

  • Padilla-Walker, L. M., Harper, J. M., & Jensen, A. C. (2010). Self-regulation as a mediator between sibling relationship quality and early adolescents’ positive and negative outcomes. Journal of family Psychology, 24(4), 419. https://doi.org/10.1037/a0020387.

    Article  Google Scholar 

  • Paris, A. H., & Paris, S. G. (2003). Assessing narrative comprehension in young children. Reading Research Quarterly, 38(1), 36–76. https://doi.org/10.1598/RRQ.38.1.3.

    Article  Google Scholar 

  • Pea, R. D., & Kurland, D. M. (1984). On the cognitive effects of learning computer programming. New Ideas in Psychology, 2, 137–168. https://doi.org/10.1016/0732-118X(84)90018-7.

    Article  Google Scholar 

  • Peters-Burton, E. E., Cleary, T. J., & Kitsantas, A. (2015). The Development of Computational Thinking in the Context of Science and Engineering Practices: A Self-Regulated Learning Approach. International Association for Development of the Information Society, 257–261.

  • Piaget, J. (1969). The child’s conception of time. Routledge & Kegan Paul.

  • Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior research methods, instruments, & computers, 36, 717–731.

  • Relkin, E., de Ruiter, L., & Bers, M. U. (2020). TechCheck: Development and validation of an unplugged assessment of computational thinking in early childhood education. Journal of Science Education and Technology, 29, 482–498. https://doi.org/10.1007/s10956-020-09831-x.

    Article  Google Scholar 

  • Resnick, M., Martin, F., Berg, R., Borovoy, R., Colella, V., Kramer, K., & Silverman, B. (1998, January). Digital manipulatives: new toys to think with. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 281–287).

  • Richland, L. E., Chan, T. K., Morrison, R. G., & Au, T. K. F. (2010). Young children’s analogical reasoning across cultures: Similarities and differences. Journal of Experimental Child Psychology, 105(1–2), 146–153. https://doi.org/10.1016/j.jecp.2009.08.003.

    Article  Google Scholar 

  • Robertson, J., Gray, S., Toye, M., & Booth, J. (2020). The relationship between executive functions and computational thinking. International Journal of Computer Science Education in Schools, 3(4), 35–49. https://doi.org/10.21585/ijcses.v3i4.76.

    Article  Google Scholar 

  • Román-González, M., Pérez-González, J. C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the computational thinking test. Computers in Human Behavior, 72, 678–691. https://doi.org/10.1016/j.chb.2016.08.047.

    Article  Google Scholar 

  • Sands, P., Yadav, A., & Good, J. (2018). Computational thinking in K-12: In-service teacher perceptions of computational thinking. In M. Khine (Ed.), Computational thinking in the STEM disciplines. Springer. https://doi.org/10.1007/978-3-319-93566-98.

  • Schmitt, S. A., Pratt, M. E., & McClelland, M. M. (2014). Examining the validity of behavioral self-regulation tools in predicting preschoolers’ academic achievement. Early Education and Development, 25(5), 641–660. https://doi.org/10.1080/10409289.2014.850397.

    Article  Google Scholar 

  • Shell, D. F., & Soh, L. K. (2013). Profiles of motivated self-regulation in college computer science courses: Differences in major versus required non-major courses. Journal of Science Education and Technology, 22, 899–913.

    Article  Google Scholar 

  • Shell, D. F., Hazley, M. P., Soh, L. K., Ingraham, E., & Ramsay, S. (2013, October). Associations of students’ creativity, motivation, and self-regulation with learning and achievement in college computer science courses. In 2013 IEEE Frontiers in Education Conference (FIE) (pp. 1637–1643). https://doi.org/10.1109/FIE.2013.6685116

  • Shonkoff, J. P., Duncan, G. J., Fisher, P. A., Magnuson, K., & Raver, C. (2011). Building the brain’s“air traffic control” system: How early experiences shape the development of executive function (Working Paper No. 11). http://www.developingchild.harvard.edu

  • Sun, L., Hu, L., Yang, W., Zhou, D., & Wang, X. (2021). STEM learning attitude predicts computational thinking skills among primary school students. Journal of Computer Assisted Learning, 37(2), 346–358. https://doi.org/10.1111/jcal.12493.

    Article  Google Scholar 

  • Wang, X. C., Choi, Y., Benson, K., Eggleston, C., & Weber, D. (2021). Teacher’s role in fostering preschoolers’ computational thinking: An exploratory case study. Early Education and Development, 32(1), 26–48. https://doi.org/10.1080/10409289.2020.1759012.

    Article  Google Scholar 

  • Wanless, S. B., Kim, K. H., Zhang, C., Degol, J. L., Chen, J. L., & Chen, F. M. (2016). Trajectories of behavioral regulation for taiwanese children from 3.5 to 6 years and relations to math and vocabulary outcomes. Early Childhood Research Quarterly, 34, 104–114. https://doi.org/10.1016/j.ecresq.2015.10.001.

    Article  Google Scholar 

  • Wing, J. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://dl.acm.org/doi/fullHtml/10.1145/1118178.1118215

  • Wing, J. (2011). Research notebook: Computational thinking—what and why. The Link Magazine, 6, 20–23. https://www.cs.cmu.edu/link/research-notebook-computational-thinking-what-and-why.

    Google Scholar 

  • Yang, W. (2018). Early childhood curriculum as cultural practice: A comparative study of school-based curriculum development in Hong Kong and Shenzhen PhD thesis. The University of Hong Kong.

  • Yang, W., & Li, H. (2020). The role of culture in early childhood curriculum development: A case study of curriculum innovations in Hong Kong kindergartens. Contemporary Issues in Early Childhoodhttps://doi.org/10.1177%2F1463949119900359.

  • Zelazo, P. D., Carter, A., Reznick, J. S., & Frye, D. (1997). Early development of executive function: A problem-solving framework. Review of General Psychology, 1(2), 198–226. https://doi.org/10.1037%2F1089-2680.1.2.198.

    Article  Google Scholar 

  • Zimmerman, B. J. (2001). Theories of self-regulated learning and academic achievement: An overview and analysis. In B. J. Zimmerman, & D. H. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (pp. 1–65). Lawrence Erlbaum.

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  1. College of Preschool Education, Capital Normal University, Hai Dian District, Beijing, China

    Hongyu Gao & Yunxiao Jiang

  2. Department of Early Childhood Education, The Education University of Hong Kong, Room B2-2/F-27, 10 Lo Ping Road, Hong Kong, China

    Weipeng Yang

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Gao, H., Yang, W. & Jiang, Y. Computational thinking in early childhood is underpinned by sequencing ability and self-regulation: a cross-sectional study. Educ Inf Technol 28, 14747–14765 (2023). https://doi.org/10.1007/s10639-023-11787-5

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